πŸ”„ Iterative Analysis Report: Exploratory Data Analysis (EDA)

🎯 Process Overview

This report shows the complete 4-step iterative process: 1. Planner: Strategic planning and task decomposition 2. Developer: Initial implementation 3. Auditor: Review and feedback 4. Developer: Refined implementation

πŸ”§ Phase: Data Overview and Summary Statistics

πŸ–₯ Execution Results

Status: ❌ Failed

Numerical Features Summary Statistics:
                Area    Perimeter  Major_Axis_Length  Minor_Axis_Length  \
count    2500.000000  2500.000000        2500.000000        2500.000000   
mean    80658.220800  1130.279015         456.601840         225.794921   
std     13664.510228   109.256418          56.235704          23.297245   
min     47939.000000   868.485000         320.844600         152.171800   
25%     70765.000000  1048.829750         414.957850         211.245925   
50%     79076.000000  1123.672000         449.496600         224.703100   
75%     89757.500000  1203.340500         492.737650         240.672875   
max    136574.000000  1559.450000         661.911300         305.818000   

         Convex_Area  Equiv_Diameter  Eccentricity     Solidity       Extent  \
count    2500.000000     2500.000000   2500.000000  2500.000000  2500.000000   
mean    81508.084400      319.334230      0.860879     0.989492     0.693205   
std     13764.092788       26.891920      0.045167     0.003494     0.060914   
min     48366.000000      247.058400      0.492100     0.918600     0.468000   
25%     71512.000000      300.167975      0.831700     0.988300     0.658900   
50%     79872.000000      317.305350      0.863700     0.990300     0.713050   
75%     90797.750000      338.057375      0.897025     0.991500     0.740225   
max    138384.000000      417.002900      0.948100     0.994400     0.829600   

         Roundness  Aspect_Ration  Compactness  
count  2500.000000    2500.000000  2500.000000  
mean      0.791533       2.041702     0.704121  
std       0.055924       0.315997     0.053067  
min       0.554600       1.148700     0.560800  
25%       0.751900       1.801050     0.663475  
50%       0.797750       1.984200     0.707700  
75%       0.834325       2.262075     0.743500  
max       0.939600       3.144400     0.904900  

Categorical Feature 'Class' Summary:
Unique values: 2
Frequency distribution:
Class
Γ‡erΓ§evelik       1300
Ürgüp Sivrisi    1200
Name: count, dtype: int64

πŸ“Š Process Summary


πŸ”§ Phase: Missing Values and Data Types Check

πŸ–₯ Execution Results

Status: ❌ Failed

Missing Values per Column:
Area                 0
Perimeter            0
Major_Axis_Length    0
Minor_Axis_Length    0
Convex_Area          0
Equiv_Diameter       0
Eccentricity         0
Solidity             0
Extent               0
Roundness            0
Aspect_Ration        0
Compactness          0
Class                0
dtype: int64

Data Types of Each Column:
Area                   int64
Perimeter            float64
Major_Axis_Length    float64
Minor_Axis_Length    float64
Convex_Area            int64
Equiv_Diameter       float64
Eccentricity         float64
Solidity             float64
Extent               float64
Roundness            float64
Aspect_Ration        float64
Compactness          float64
Class                 object
dtype: object

πŸ“Š Process Summary


πŸ”§ Phase: Distribution Analysis of Numerical Features

πŸ–₯ Execution Results

Status: ❌ Failed

Skewness of Numerical Features:
Area                 0.495701
Perimeter            0.414290
Major_Axis_Length    0.502678
Minor_Axis_Length    0.104241
Convex_Area          0.493719
Equiv_Diameter       0.271704
Eccentricity        -0.748174
Solidity            -5.687594
Extent              -1.025952
Roundness           -0.372463
Aspect_Ration        0.547902
Compactness         -0.062339
dtype: float64

Kurtosis (Excess) of Numerical Features:
Area                  0.126339
Perimeter            -0.024205
Major_Axis_Length    -0.018057
Minor_Axis_Length     0.070689
Convex_Area           0.120381
Equiv_Diameter       -0.148808
Eccentricity          1.788224
Solidity             80.957095
Extent                0.421733
Roundness            -0.241156
Aspect_Ration        -0.205354
Compactness          -0.502231
dtype: float64

πŸ“ˆ Generated Visualizations

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πŸ“Š Process Summary


πŸ”§ Phase: Correlation Analysis Among Features

πŸ–₯ Execution Results

Status: ❌ Failed

Correlation Matrix among Numerical Features:
                       Area  Perimeter  Major_Axis_Length  Minor_Axis_Length  \
Area               1.000000   0.928548           0.789133           0.685304   
Perimeter          0.928548   1.000000           0.946181           0.392913   
Major_Axis_Length  0.789133   0.946181           1.000000           0.099376   
Minor_Axis_Length  0.685304   0.392913           0.099376           1.000000   
Convex_Area        0.999806   0.929971           0.789061           0.685634   
Equiv_Diameter     0.998464   0.928055           0.787078           0.690020   
Eccentricity       0.159624   0.464601           0.704287          -0.590877   
Solidity           0.158388   0.065340           0.119291           0.090915   
Extent            -0.014018  -0.140600          -0.214990           0.233576   
Roundness         -0.149378  -0.500968          -0.684972           0.558566   
Aspect_Ration      0.159960   0.487880           0.729156          -0.598475   
Compactness       -0.160438  -0.484440          -0.726958           0.603441   

                   Convex_Area  Equiv_Diameter  Eccentricity  Solidity  \
Area                  0.999806        0.998464      0.159624  0.158388   
Perimeter             0.929971        0.928055      0.464601  0.065340   
Major_Axis_Length     0.789061        0.787078      0.704287  0.119291   
Minor_Axis_Length     0.685634        0.690020     -0.590877  0.090915   
Convex_Area           1.000000        0.998289      0.159156  0.139178   
Equiv_Diameter        0.998289        1.000000      0.156246  0.159454   
Eccentricity          0.159156        0.156246      1.000000  0.043991   
Solidity              0.139178        0.159454      0.043991  1.000000   
Extent               -0.015449       -0.010970     -0.327316  0.067537   
Roundness            -0.153615       -0.145313     -0.890651  0.200836   
Aspect_Ration         0.159822        0.155762      0.950225  0.026410   
Compactness          -0.160432       -0.156411     -0.981689 -0.019967   

                     Extent  Roundness  Aspect_Ration  Compactness  
Area              -0.014018  -0.149378       0.159960    -0.160438  
Perimeter         -0.140600  -0.500968       0.487880    -0.484440  
Major_Axis_Length -0.214990  -0.684972       0.729156    -0.726958  
Minor_Axis_Length  0.233576   0.558566      -0.598475     0.603441  
Convex_Area       -0.015449  -0.153615       0.159822    -0.160432  
Equiv_Diameter    -0.010970  -0.145313       0.155762    -0.156411  
Eccentricity      -0.327316  -0.890651       0.950225    -0.981689  
Solidity           0.067537   0.200836       0.026410    -0.019967  
Extent             1.000000   0.352338      -0.329933     0.336984  
Roundness          0.352338   1.000000      -0.935233     0.933308  
Aspect_Ration     -0.329933  -0.935233       1.000000    -0.990778  
Compactness        0.336984   0.933308      -0.990778     1.000000

πŸ“ˆ Generated Visualizations

Visualization 1

πŸ“Š Process Summary


πŸ”§ Phase: Class Distribution Analysis

πŸ–₯ Execution Results

Status: ❌ Failed

Class Distribution Counts:
Class
Γ‡erΓ§evelik       1300
Ürgüp Sivrisi    1200
Name: count, dtype: int64

πŸ“ˆ Generated Visualizations

Visualization 1

πŸ“Š Process Summary


πŸ”§ Phase: Outlier Detection in Numerical Features

πŸ–₯ Execution Results

Status: ❌ Failed

Outlier Detection and Analysis in Numerical Features

Outlier Summary (IQR and Z-score methods):
          Feature  IQR_Outliers_Count  IQR_Outliers_%  Zscore_Outliers_Count  Zscore_Outliers_%     Mean_All      Std_All  Mean_wo_Outliers  Std_wo_Outliers
             Area                  18            0.72                     13               0.52 80658.220800 13664.510228      80331.083400     13152.687709
        Perimeter                  16            0.64                      8               0.32  1130.279015   109.256418       1128.082581       106.080663
Major_Axis_Length                  21            0.84                      8               0.32   456.601840    56.235704        455.168829        54.250506
Minor_Axis_Length                  30            1.20                      9               0.36   225.794921    23.297245        225.731180        22.258129
      Convex_Area                  17            0.68                     13               0.52 81508.084400 13764.092788      81194.389448     13269.303919
   Equiv_Diameter                  13            0.52                      9               0.36   319.334230    26.891920        318.891348        26.248731
     Eccentricity                  18            0.72                     14               0.56     0.860879     0.045167          0.862081         0.042827
         Solidity                 103            4.12                     29               1.16     0.989492     0.003494          0.989957         0.002157
           Extent                  46            1.84                     13               0.52     0.693205     0.060914          0.696548         0.056276
        Roundness                   5            0.20                      4               0.16     0.791533     0.055924          0.791916         0.055307
    Aspect_Ration                  11            0.44                      8               0.32     2.041702     0.315997          2.037341         0.309768
      Compactness                   2            0.08                      2               0.08     0.704121     0.053067          0.703975         0.052836

Analysis Notes:
- IQR method identifies moderate outliers based on quartiles.
- Z-score method identifies extreme outliers beyond 3 standard deviations.
- Comparing mean and std with and without outliers shows their influence on distribution.
- Features with substantial outliers may require treatment such as capping or removal.
- Visualizations help confirm the presence and spread of outliers for each feature.

πŸ“ˆ Generated Visualizations

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πŸ“Š Process Summary


πŸ”§ Phase: Feature Relationships Visualization

πŸ–₯ Execution Results

Status: ❌ Failed

### Scatter Plots: Selected Numerical Features vs Target Class ###
- Scatter plot of Perimeter vs Area shows how classes separate or cluster in this feature space.
- Scatter plot of Minor_Axis_Length vs Major_Axis_Length shows how classes separate or cluster in this feature space.
- Scatter plot of Equiv_Diameter vs Convex_Area shows how classes separate or cluster in this feature space.
- Scatter plot of Aspect_Ration vs Roundness shows how classes separate or cluster in this feature space.

### Pair Plot: All Numerical Features Colored by Class ###
- Pair plot reveals pairwise relationships and class separability patterns across all numerical features.

### Violin Plots: Distribution of Numerical Features by Class ###
- Violin plot of Area shows distribution shape and differences between classes.
- Violin plot of Perimeter shows distribution shape and differences between classes.
- Violin plot of Major_Axis_Length shows distribution shape and differences between classes.
- Violin plot of Minor_Axis_Length shows distribution shape and differences between classes.
- Violin plot of Convex_Area shows distribution shape and differences between classes.
- Violin plot of Equiv_Diameter shows distribution shape and differences between classes.
- Violin plot of Eccentricity shows distribution shape and differences between classes.
- Violin plot of Solidity shows distribution shape and differences between classes.
- Violin plot of Extent shows distribution shape and differences between classes.
- Violin plot of Roundness shows distribution shape and differences between classes.
- Violin plot of Aspect_Ration shows distribution shape and differences between classes.
- Violin plot of Compactness shows distribution shape and differences between classes.

Visualizations complete. Review plots for patterns such as clustering, separability, and distribution differences between classes.

πŸ“ˆ Generated Visualizations

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πŸ“Š Process Summary


πŸ”§ Phase: Data Quality and Consistency Checks

πŸ–₯ Execution Results

Status: ❌ Failed

### Data Quality and Consistency Checks ###

Number of duplicate records in the dataset: 0

---

Checking numerical feature value ranges against min and max from summary statistics:

          Feature  Count_Below_Min  Count_Above_Max
             Area                0                0
        Perimeter                0                0
Major_Axis_Length                0                0
Minor_Axis_Length                0                0
      Convex_Area                0                0
   Equiv_Diameter                0                0
     Eccentricity                0                0
         Solidity                0                0
           Extent                0                0
        Roundness                0                0
    Aspect_Ration                0                0
      Compactness                0                0

No values should be below min or above max as these are dataset min/max.

Logical consistency checks between related features:

Records where Area > Convex_Area: 0
Records where Perimeter < Major_Axis_Length: 0
Records where Perimeter < Minor_Axis_Length: 0
Records where Area differs from ellipse area approximation by >30%: 0
Records where Aspect_Ration differs from Major_Axis_Length/Minor_Axis_Length by >0.1: 0

Summary of logical inconsistencies:
- Area > Convex_Area: 0 records
- Perimeter < Major_Axis_Length: 0 records
- Perimeter < Minor_Axis_Length: 0 records
- Area vs Ellipse area difference >30%: 0 records
- Aspect_Ration inconsistent with axes ratio >0.1: 0 records

πŸ“ˆ Generated Visualizations

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πŸ“Š Process Summary


πŸ“ˆ Overall Process Summary